Generative Modeling
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Deep Generative Modeling
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Author : Jakub M. Tomczak
language : en
Publisher: Springer Nature
Release Date : 2022-02-18
Deep Generative Modeling written by Jakub M. Tomczak and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-18 with Computers categories.
This textbook tackles the problem of formulating AI systems by combining probabilistic modeling and deep learning. Moreover, it goes beyond typical predictive modeling and brings together supervised learning and unsupervised learning. The resulting paradigm, called deep generative modeling, utilizes the generative perspective on perceiving the surrounding world. It assumes that each phenomenon is driven by an underlying generative process that defines a joint distribution over random variables and their stochastic interactions, i.e., how events occur and in what order. The adjective "deep" comes from the fact that the distribution is parameterized using deep neural networks. There are two distinct traits of deep generative modeling. First, the application of deep neural networks allows rich and flexible parameterization of distributions. Second, the principled manner of modeling stochastic dependencies using probability theory ensures rigorous formulation and prevents potential flaws in reasoning. Moreover, probability theory provides a unified framework where the likelihood function plays a crucial role in quantifying uncertainty and defining objective functions. Deep Generative Modeling is designed to appeal to curious students, engineers, and researchers with a modest mathematical background in undergraduate calculus, linear algebra, probability theory, and the basics in machine learning, deep learning, and programming in Python and PyTorch (or other deep learning libraries). It will appeal to students and researchers from a variety of backgrounds, including computer science, engineering, data science, physics, and bioinformatics, who wish to become familiar with deep generative modeling. To engage the reader, the book introduces fundamental concepts with specific examples and code snippets. The full code accompanying the book is available on github. The ultimate aim of the book is to outline the most important techniques in deep generative modeling and, eventually, enable readers to formulate new models and implement them.
Generative Deep Learning
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Author : David Foster
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2019-06-28
Generative Deep Learning written by David Foster and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-28 with Computers categories.
Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative. Discover how variational autoencoders can change facial expressions in photos Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation Create recurrent generative models for text generation and learn how to improve the models using attention Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN
Deep Generative Models
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Author : Anirban Mukhopadhyay
language : en
Publisher: Springer Nature
Release Date : 2024-10-08
Deep Generative Models written by Anirban Mukhopadhyay and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-08 with Computers categories.
This book constitutes the proceedings of the 4th workshop on Deep Generative Models for Medical Image Computing and Computer Assisted Intervention, DGM4MICCAI 2024, held in conjunction with the 27th International conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024, in Marrakesh, Morocco in October 2024. The 21 papers presented here were carefully reviewed and selected from 40 submissions. These papers deal with a broad range of topics, ranging from methodology (such as Causal inference, Latent interpretation, Generative factor analysis) to Applications (such as Mammography, Vessel imaging, Surgical videos and more).
Deep Generative Modeling
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Author : Jakub M. Tomczak
language : en
Publisher: Springer
Release Date : 2022-03-16
Deep Generative Modeling written by Jakub M. Tomczak and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-16 with Computers categories.
This textbook tackles the problem of formulating AI systems by combining probabilistic modeling and deep learning. Moreover, it goes beyond typical predictive modeling and brings together supervised learning and unsupervised learning. The resulting paradigm, called deep generative modeling, utilizes the generative perspective on perceiving the surrounding world. It assumes that each phenomenon is driven by an underlying generative process that defines a joint distribution over random variables and their stochastic interactions, i.e., how events occur and in what order. The adjective "deep" comes from the fact that the distribution is parameterized using deep neural networks. There are two distinct traits of deep generative modeling. First, the application of deep neural networks allows rich and flexible parameterization of distributions. Second, the principled manner of modeling stochastic dependencies using probability theory ensures rigorous formulation and prevents potential flaws in reasoning. Moreover, probability theory provides a unified framework where the likelihood function plays a crucial role in quantifying uncertainty and defining objective functions. Deep Generative Modeling is designed to appeal to curious students, engineers, and researchers with a modest mathematical background in undergraduate calculus, linear algebra, probability theory, and the basics in machine learning, deep learning, and programming in Python and PyTorch (or other deep learning libraries). It will appeal to students and researchers from a variety of backgrounds, including computer science, engineering, data science, physics, and bioinformatics, who wish to become familiar with deep generative modeling. To engage the reader, the book introduces fundamental concepts with specific examples and code snippets. The full code accompanying the book is available on github. The ultimate aim of the book is to outline the most important techniques in deep generative modeling and, eventually, enable readers to formulate new models and implement them.
Artificial Intelligence And Large Language Models
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Author : Kutub Thakur
language : en
Publisher: CRC Press
Release Date : 2024-07-12
Artificial Intelligence And Large Language Models written by Kutub Thakur and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-12 with Computers categories.
Having been catapulted into public discourse in the last few years, this book serves as an in-depth exploration of the ever-evolving domain of artificial intelligence (AI), large language models, and ChatGPT. It provides a meticulous and thorough analysis of AI, ChatGPT technology, and their prospective trajectories given the current trend, in addition to tracing the significant advancements that have materialized over time. Key Features: Discusses the fundamentals of AI for general readers Introduces readers to the ChatGPT chatbot and how it works Covers natural language processing (NLP), the foundational building block of ChatGPT Introduces readers to the deep learning transformer architecture Covers the fundamentals of ChatGPT training for practitioners Illustrated and organized in an accessible manner, this textbook contains particular appeal to students and course convenors at the undergraduate and graduate level, as well as a reference source for general readers.
Pytorch Essentials A Comprehensive Guide To Machine Learning Techniques
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Author : Adam Jones
language : en
Publisher: Walzone Press
Release Date : 2024-12-03
Pytorch Essentials A Comprehensive Guide To Machine Learning Techniques written by Adam Jones and has been published by Walzone Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-03 with Computers categories.
"PyTorch Essentials: A Comprehensive Guide to Machine Learning Techniques" is an essential resource for programmers, researchers, and enthusiasts eager to enhance their mastery of machine learning and artificial intelligence through the PyTorch framework. This guide deftly combines theoretical foundations with hands-on applications, covering everything from basic tensor manipulation and neural network creation to sophisticated architectures such as CNNs, RNNs, and generative models. Additionally, it delves into reinforcement learning, optimization strategies, and best practices for deployment, empowering readers with the skills to develop scalable, production-ready solutions. Whether advancing your research or solving real-world industry challenges, this book serves as your indispensable guide to mastering machine learning with PyTorch.
Cybermedics
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Author : Varun Sapra
language : en
Publisher: CRC Press
Release Date : 2025-03-18
Cybermedics written by Varun Sapra and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-18 with Computers categories.
CyberMedics: Navigating AI and Security in the Medical Field is a comprehensive exploration of the transformative role of artificial intelligence (AI) in healthcare and the critical importance of securing medical data in an increasingly digital world. As AI technologies revolutionize diagnostics, treatment planning, and patient care, they also introduce new challenges related to data privacy, ethical considerations, and cybersecurity. This book offers a balanced perspective, providing healthcare professionals, technologists, and policymakers with the tools they need to understand and navigate the intersection of AI and security. Through detailed case studies, expert insights, and practical guidance, readers will discover how AI can be leveraged to improve patient outcomes while maintaining the highest standards of data protection. Key topics include: AI in healthcare, exploring how emerging technologies like machine learning, natural language processing, and predictive analytics are poised to reshape the industry the ethical implications of AI in healthcare strategies for safeguarding sensitive information against cyber threats future frameworks governing the use of AI in medical settings Whether you’re on the front lines of patient care, involved in healthcare IT, or shaping policy, CyberMedics: Navigating AI and Security in the Medical Field equips you with the knowledge to make informed decisions in a rapidly evolving landscape, ensuring that innovation and security go hand in hand to enhance the quality of care.
Advances In Multimedia Modeling
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Author : Kuo-Tien Lee
language : en
Publisher: Springer
Release Date : 2011-01-10
Advances In Multimedia Modeling written by Kuo-Tien Lee and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-01-10 with Computers categories.
This two-volume proceedings constitutes the refereed papers of the 17th International Multimedia Modeling Conference, MMM 2011, held in Taipei, Taiwan, in January 2011. The 51 revised regular papers, 25 special session papers, 21 poster session papers, and 3 demo session papers, were carefully reviewed and selected from 450 submissions. The papers are organized in topical sections on audio, image video processing, coding and compression; media content browsing and retrieval; multi-camera, multi-view, and 3D systems; multimedia indexing and mining; multimedia content analysis; multimedia signal processing and communications; and multimedia applications. The special session papers deal with content analysis for human-centered multimedia applications; large scale rich media data management; multimedia understanding for consumer electronics; image object recognition and compression; and interactive image and video search.
Large Language Models
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Author : John Atkinson-Abutridy
language : en
Publisher: CRC Press
Release Date : 2024-10-17
Large Language Models written by John Atkinson-Abutridy and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-17 with Computers categories.
This book serves as an introduction to the science and applications of Large Language Models (LLMs). You'll discover the common thread that drives some of the most revolutionary recent applications of artificial intelligence (AI): from conversational systems like ChatGPT or BARD, to machine translation, summary generation, question answering, and much more. At the heart of these innovative applications is a powerful and rapidly evolving discipline, natural language processing (NLP). For more than 60 years, research in this science has been focused on enabling machines to efficiently understand and generate human language. The secrets behind these technological advances lie in LLMs, whose power lies in their ability to capture complex patterns and learn contextual representations of language. How do these LLMs work? What are the available models and how are they evaluated? This book will help you answer these and many other questions. With a technical but accessible introduction: •You will explore the fascinating world of LLMs, from its foundations to its most powerful applications •You will learn how to build your own simple applications with some of the LLMs Designed to guide you step by step, with six chapters combining theory and practice, along with exercises in Python on the Colab platform, you will master the secrets of LLMs and their application in NLP. From deep neural networks and attention mechanisms, to the most relevant LLMs such as BERT, GPT-4, LLaMA, Palm-2 and Falcon, this book guides you through the most important achievements in NLP. Not only will you learn the benchmarks used to evaluate the capabilities of these models, but you will also gain the skill to create your own NLP applications. It will be of great value to professionals, researchers and students within AI, data science and beyond.
Deep Learning Concepts In Operations Research
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Author : Biswadip Basu Mallik
language : en
Publisher: CRC Press
Release Date : 2024-08-30
Deep Learning Concepts In Operations Research written by Biswadip Basu Mallik and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-30 with Computers categories.
The model-based approach for carrying out classification and identification of tasks has led to the pervading progress of the machine learning paradigm in diversified fields of technology. Deep Learning Concepts in Operations Research looks at the concepts that are the foundation of this model-based approach. Apart from the classification process, the machine learning (ML) model has become effective enough to predict future trends of any sort of phenomena. Such fields as object classification, speech recognition, and face detection have sought extensive application of artificial intelligence (AI) and ML as well. Among a variety of topics, the book examines: An overview of applications and computing devices Deep learning impacts in the field of AI Deep learning as state-of-the-art approach to AI Exploring deep learning architecture for cutting-edge AI solutions Operations research is the branch of mathematics for performing many operational tasks in other allied domains, and the book explains how the implementation of automated strategies in optimization and parameter selection can be carried out by AI and ML. Operations research has many beneficial aspects for decision making. Discussing how a proper decision depends on several factors, the book examines how AI and ML can be used to model equations and define constraints to solve problems and discover proper and valid solutions more easily. It also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost.